TY - GEN
T1 - Structured GANs
AU - Peleg, Irad
AU - Wolf, Lior
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - We present Generative Adversarial Networks (GANs), in which the symmetric property of the generated images is controlled. This is obtained through the generator network's architecture, while the training procedure and the loss remain the same. The symmetric GANs are applied to face image synthesis in order to generate novel faces with a varying amount of symmetry. We also present an unsupervised face rotation capability, which is based on the novel notion of one-shot fine tuning.
AB - We present Generative Adversarial Networks (GANs), in which the symmetric property of the generated images is controlled. This is obtained through the generator network's architecture, while the training procedure and the loss remain the same. The symmetric GANs are applied to face image synthesis in order to generate novel faces with a varying amount of symmetry. We also present an unsupervised face rotation capability, which is based on the novel notion of one-shot fine tuning.
UR - http://www.scopus.com/inward/record.url?scp=85050954207&partnerID=8YFLogxK
U2 - 10.1109/WACV.2018.00084
DO - 10.1109/WACV.2018.00084
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AN - SCOPUS:85050954207
T3 - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
SP - 719
EP - 728
BT - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Y2 - 12 March 2018 through 15 March 2018
ER -